Objective The objective of this study was to compare the vertical (vGRF), anterior‐posterior (apGRF), and medial‐lateral (mlGRF) ground reaction force (GRF) profiles throughout the stance phase of gait (1) between individuals 6 to 12 months post–anterior cruciate ligament reconstruction (ACLR) and uninjured matched controls and (2) between ACLR and individuals with differing radiographic severities of knee osteoarthritis (KOA), defined as Kellgren and Lawrence (KL) grades KL2, KL3, and KL4. Methods A total of 196 participants were included in this retrospective cross‐sectional analysis. Gait biomechanics were collected from individuals 6 to 12 months post‐ACLR (n = 36), uninjured controls matched to the ACLR group (n = 36), and individuals with KL2 (n = 31), KL3 (n = 67), and KL4 osteoarthritis (OA) (n = 26). Between‐group differences in vGRF, apGRF, and mlGRF were assessed in reference to the ACLR group throughout each percentage of stance phase using a functional linear model. Results The ACLR group demonstrated lower vGRF and apGRF in early and late stance compared to the uninjured controls, with large effects (Cohen's d range: 1.35–1.66). Conversely, the ACLR group exhibited greater vGRF (87%–90%; 4.88% body weight [BW]; d = 0.75) and apGRF (84%–94%; 2.41% BW; d = 0.79) than the KL2 group in a small portion of late stance. No differences in mlGRF profiles were observed between the ACLR and either the uninjured controls or the KL2 group. The magnitude of difference in GRF profiles between the ACLR and OA groups increased with OA disease severity. Conclusion Individuals 6 to 12 months post‐ACLR exhibit strikingly similar GRF profiles as individuals with KL2 KOA, suggesting both patient groups may benefit from targeted interventions to address aberrant GRF profiles. image

Objective:To investigate the relationship between measures of radiographic joint space width (JSW) loss and magnetic resonance imaging (MRI)-based cartilage thickness loss in the medial weight-bearing region of the tibiofemoral joint over 12-24 months. To stratify this relationship by clinically meaningful subgroups (sex and pain status). Design:We analyzed a subset of knees (n ​= ​256) from the Osteoarthritis Initiative (OAI) likely in early stage OA based on joint space narrowing (JSN) measurements. Natural logarithm transformation was used to approximate near normal distributions for JSW loss. Pearson Correlation coefficients described the relationship between ln-transformed JSW loss and several versions of deep learning-derived MRI-based cartilage thickness loss parameters (minimum, maximum, and mean) in subregions of the femoral condyle, tibial plateau, and combined femoral and tibial regions. Linear mixed-effects models evaluated the associations between the ln-transformed radiographic and MRI-derived measures including potential confounders. Results:We found weak correlations between ln-transformed JSW loss and MRI-based cartilage thickness ranging from R ​= ​-0.13 (p ​= ​0.20) to R ​= ​0.26 (p ​< ​0.01). Correlations were higher (still poor) among females compared to males and painful compared to non-painful knees. Model results showed weak associations for nearly all MRI-based measures, ranging from no association to β (95% CI) ​= ​0.25 (0.11, 0.39). Associations were higher among females compared to males and minimal differences between painful and non-painful knees. Conclusions:Despite its recommended use in disease-modifying OA drug clinical trials, results suggest that JSW loss is an ineffective proxy measure of cartilage thickness loss over 12-24 months and within a localized region of the tibiofemoral joint.

Knee osteoarthritis (OA), a prevalent joint disease in the U.S., poses challenges in terms of predicting of its early progression. Although high-resolution knee magnetic resonance imaging (MRI) facilitates more precise OA diagnosis, the heterogeneous and multifactorial aspects of OA pathology remain significant obstacles for prognosis. MRI-based scoring systems, while standardizing OA assessment, are both time-consuming and labor-intensive. Current AI technologies facilitate knee OA risk scoring and progression prediction, but these often focus on the symptomatic phase of OA, bypassing initial-stage OA prediction. Moreover, their reliance on complex algorithms can hinder clinical interpretation. To this end, we make this effort to construct a computationally efficient, easily-interpretable, and state-of-the-art approach aiding in the radiographic OA (rOA) auto-classification and prediction of the incidence and progression, by contrasting an individual's cartilage thickness with a similar demographic in the rOA-free cohort. To better visualize, we have developed the toolset for both prediction and local visualization. A movie demonstrating different subtypes of dynamic changes in local centile scores during rOA progression is available at https://tli3.github.io/KneeOA/. Specifically, we constructed age-BMI-dependent reference charts for knee OA cartilage thickness, based on MRI scans from 957 radiographic OA (rOA)-free individuals from the Osteoarthritis Initiative cohort. Then we extracted local and global centiles by contrasting an individual's cartilage thickness to the rOA-free cohort with a similar age and BMI. Using traditional boosting approaches with our centile-based features, we obtain rOA classification of KLG ≤ 1 versus KLG = 2 (AUC = 0.95, F1 = 0.89), KLG ≤ 1 versus KLG ≥ 2 (AUC = 0.90, F1 = 0.82) and prediction of KLG2 progression (AUC = 0.98, F1 = 0.94), rOA incidence (KLG increasing from < 2 to ≥ 2; AUC = 0.81, F1 = 0.69) and rOA initial transition (KLG from 0 to 1; AUC = 0.64, F1 = 0.65) within a future 48-month period. Such performance in classifying KLG ≥ 2 matches that of deep learning methods in recent literature. Furthermore, its clinical interpretation suggests that cartilage changes, such as thickening in lateral femoral and anterior femoral regions and thinning in lateral tibial regions, may serve as indicators for prediction of rOA incidence and early progression. Meanwhile, cartilage thickening in the posterior medial and posterior lateral femoral regions, coupled with a reduction in the central medial femoral region, may signify initial phases of rOA transition.

Objective: To employ novel methodologies to identify phenotypes in knee OA based on variation among three baseline data blocks: 1) femoral cartilage thickness, 2) tibial cartilage thickness, and 3) participant characteristics and clinical features. Methods: Baseline data were from 3321 Osteoarthritis Initiative (OAI) participants with available cartilage thickness maps (6265 knees) and 77 clinical features. Cartilage maps were obtained from 3D DESS MR images using a deep-learning based segmentation approach and an atlas-based analysis developed by our group. Anglebased Joint and Individual Variation Explained (AJIVE) was used to capture and quantify variation, both shared among multiple data blocks and individual to each block, and to determine statistical significance. Results: Three major modes of variation were shared across the three data blocks. Mode 1 reflected overall thicker cartilage among men, those with higher education, and greater knee forces; Mode 2 showed associations between worsening Kellgren-Lawrence Grade, medial cartilage thinning, and worsening symptoms; and Mode 3 contrasted lateral and medial-predominant cartilage loss associated with BMI and malalignment. Each data block also demonstrated individual, independent modes of variation consistent with the known discordance between symptoms and structure in knee OA and reflecting the importance of features such as physical function, symptoms, and comorbid conditions independent of structural damage. Conclusions: This exploratory analysis, combining the rich OAI dataset with novel methods for determining and visualizing cartilage thickness, reinforces known associations in knee OA while providing insights into the potential for data integration in knee OA phenotyping.

Purpose: Aberrant gait biomechanics that develop early following anterior cruciate ligament (ACL) injury and reconstruction (ACLR) contribute to the development of posttraumatic osteoarthritis (PTOA). Altered gait patterns are also linked to disease progression in individuals who have been diagnosed with idiopathic osteoarthritis (IOA). Both vertical (vGRF) and anterior-posterior ground reaction forces (apGRF) are critical components of lower extremity loading during walking gait that are linked to the compressive and shear forces exerted on joint tissues.

ABSTRACT Introduction Aberrant gait variability has been observed after anterior cruciate ligament reconstruction (ACLR), yet it remains unknown if gait variability is associated with early changes in cartilage composition linked to osteoarthritis development. Our purpose was to determine the association between femoral articular cartilage T1ρ magnetic resonance imaging relaxation times and gait variability. Methods T1ρ magnetic resonance imaging and gait kinematics were collected in 22 ACLR participants (13 women; 21 ± 4 yr old; 7.52 ± 1.43 months post-ACLR). Femoral articular cartilage from the ACLR and uninjured limbs were segmented into anterior, central, and posterior regions from the weight-bearing portions of the medial and lateral condyles. Mean T1ρ relaxation times were extracted from each region and interlimb ratios (ILR) were calculated (i.e., ACLR/uninjured limb). Greater T1ρ ILR values were interpreted as less proteoglycan density (worse cartilage composition) in the injured limb compared with the uninjured limb. Knee kinematics were collected at a self-selected comfortable walking speed on a treadmill with an eight-camera three-dimensional motion capture system. Frontal and sagittal plane kinematics were extracted, and sample entropy was used to calculate kinematic variability structure (KVstructure). Pearson’s product–moment correlations were conducted to determine the associations between T1ρ and KVstructure variables. Results Lesser frontal plane KVstructure was associated with greater mean T1ρ ILR in the anterior lateral (r = −0.44, P = 0.04) and anterior medial condyles (r = −0.47, P = 0.03). Lesser sagittal plane KVstructure was associated with greater mean T1ρ ILR in the anterior lateral condyle (r = −0.47, P = 0.03). Conclusions The association between less KVstructure and worse femoral articular cartilage proteoglycan density suggests a link between less variable knee kinematics and deleterious changes joint tissue changes. The findings suggest that less knee kinematic variability structure is a mechanism linking aberrant gait to early osteoarthritis development.

Background Ultrasonography is capable of detecting morphological changes in femoral articular cartilage cross-sectional area in response to an acute bout of walking; yet, the response of femoral cartilage cross-sectional area varies between individuals. It is hypothesized that differences in joint kinetics may influence the response of cartilage to a standardized walking protocol. Therefore, the study purpose was to compare internal knee abduction and extension moments between individuals with anterior cruciate ligament reconstruction who demonstrate an acute increase, decrease, or unchanged medial femoral cross-sectional area response following 3000 steps. Methods The medial femoral cartilage in the anterior cruciate ligament reconstructed limb was assessed with ultrasonography before and immediately following 3000 steps of treadmill walking. Knee joint moments were calculated in the anterior cruciate ligament reconstructed limb and compared between groups throughout the stance phase of gait using linear regression and functional, mixed effects waveform analyses. Findings No associations between peak knee joint moments and the cross-sectional area response were observed. The group that demonstrated an acute cross-sectional area increase exhibited 1) lower knee abduction moments in early stance in comparison to the group that exhibited a decreased cross-sectional area response; and 2) greater knee extension moments in early stance in comparison to the group with an unchanged cross-sectional area response. Interpretation The propensity of femoral cartilage to acutely increase cross-sectional area in response to walking is consistent with less-dynamic knee abduction and knee extension moment profiles.

Objective A complex association exists between aberrant gait biomechanics and posttraumatic knee osteoarthritis (PTOA) development. Previous research has primarily focused on the link between peak loading during the loading phase of stance and joint tissue changes following anterior cruciate ligament reconstruction (ACLR). However, the associations between loading and cartilage composition at other portions of stance, including midstance and late stance, is unclear. The objective of this study was to explore associations between vertical ground reaction force (vGRF) at each 1% increment of stance phase and tibiofemoral articular cartilage magnetic resonance imaging (MRI) T1ρ relaxation times following ACLR. Design Twenty-three individuals (47.82% female, 22.1 ±4.1 years old) with unilateral ACLR participated in a gait assessment and T1ρ MRI collection at 12.25 ± 0.61 months post-ACLR. T1ρ relaxation times were calculated for the articular cartilage of the weightbearing medial and lateral femoral (MFC, LFC) and tibial (MTC, LTC) condyles. Separate bivariate, Pearson product moment correlation coefficients ( r) were used to estimate strength of associations between T1ρ MRI relaxation times in the medial and lateral tibiofemoral articular cartilage with vGRF across the entire stance phase. Results Greater vGRF during midstance (46%-56% of stance phase) was associated with greater T1ρ MRI relaxation times in the MFC ( r ranging between 0.43 and 0.46). Conclusions Biomechanical gait profiles that include greater vGRF during midstance are associated with MRI estimates of lesser proteoglycan density in the MFC. Inability to unload the ACLR limb during midstance may be linked to joint tissue changes associated with PTOA development.

Individuals with anterior cruciate ligament reconstruction (ACLR) exhibit more regular gait patterns, resulting in more rigid movements compared to controls. Aberrant joint loading post-ACLR is linked to worse T1ρ magnetic resonance imaging (MRI) relaxation times, an outcome associated with osteoarthritis development. Yet, it remains unknown if knee movement regularity during gait associates with knee cartilage composition in individuals with ACLR. PURPOSE: Determine the association between femoral articular cartilage T1ρ MRI relaxation times and knee joint movement regularity during gait. METHODS: T1ρ MRI and gait kinematics were collected in 20 participants with ACLR (13 Females; 21 ± 4 years old; 8 ± 1 months post-ACLR). Femoral articular cartilage from the ACLR and uninjured limbs was segmented into anterior, central, and posterior regions from the weightbearing portions of the medial and lateral condyles. Mean T1ρ relaxation times were extracted from the cartilage in each region and interlimb ratios were calculated (i.e., ACLR / uninjured limb). Greater interlimb T1ρ ratios were interpreted as lesser proteoglycan density (worse cartilage composition) in the injured limb compared to the uninjured limb. Knee kinematics were collected at a self-selected gait speed on a treadmill with an 8-camera 3D motion capture system. Frontal and sagittal plane knee kinematics were extracted, and sample entropy was used to calculate the degree of movement regularity (i.e., lower values reflect more regularity). Pearson’s product-moment correlations were conducted to determine the association between T1ρ and movement regularity variables. RESULTS: Greater frontal plane knee movement regularity associated with greater mean T1ρ interlimb ratios in the anterior portions of the lateral (r = -0.44, p = .04) and medial condyles (r = -0.47, p = .03). Greater sagittal plane kinematic regularity associated with greater mean T1ρ interlimb ratios in the anterior portion of the lateral condyle (r = -0.47, p = .03). CONCLUSION: Greater movement regularity was associated with less femoral articular cartilage proteoglycan density which is linked to early osteoarthritis development. We hypothesize that greater movement regularity contributes to less adaptable gait and more concentrated cartilage loading.

Purpose Anterior cruciate ligament rupture is associated with characteristic bone contusions in approximately 80% of patients, and these have been correlated with higher pain scores. Bone bruising may indicate joint damage that increases inflammation and the likelihood of posttraumatic osteoarthritis. We sought to characterize the severity of bone bruising following acute anterior cruciate ligament injury and determine if it correlates with synovial fluid and serum levels of the proinflammatory chemokine monocyte chemoattractant protein-1 associated with posttraumatic osteoarthritis. Methods This was a retrospective analysis of data collected prospectively from January 2014 through December 2016. All patients who sustained an acute ligament rupture were evaluated within 15 days of injury, obtained a magnetic resonance imaging study, and underwent bone-patellar-tendon-bone autograft reconstruction were offered enrollment. The overall severity of bone bruising on magnetic resonance imaging was graded (sum of 0–3 grades in 13 sectors of the articular surfaces). Serum and synovial fluid levels of monocyte chemoattractant protein-1 were measured within 14 days of injury, and serum levels were again measured 6 and 12 months following surgery. Separate univariate linear regression models were constructed to determine the association between monocyte chemoattractant protein-1 and bone bruising severity at each time point. Results Forty-eight subjects were included in this study. They had a mean age of 21.4 years and were 48% female. Median overall bone bruising severity was 5 (range 0–14). Severity of bone bruising correlated with higher synovial fluid concentrations of monocyte chemoattractant protein-1 preoperatively (R 2 = 0.18, p = 0.009) and with serum concentrations at 12 months post-reconstruction (R 2 = 0.12, p = 0.04). Conclusions The severity of bone bruising following anterior cruciate ligament rupture is associated with higher levels of the proinflammatory cytokine monocyte chemoattractant protein-1 in synovial fluid acutely post-injury and in serum 12-months following anterior cruciate ligament reconstruction. This suggests that severe bone bruising on magnetic resonance imaging after ligament rupture may indicate increased risk for persistent joint inflammation and posttraumatic osteoarthritis. Level of evidence III ― retrospective cohort study.

Objective To compare T1ρ relaxation times of the medial and lateral regions of the patella and femoral trochlea at 6 and 12 months following anterior cruciate ligament reconstruction (ACLR) on the ACLR and contralateral extremity. Greater T1ρ relaxation times are associated with a lower proteoglycan density of articular cartilage. Methods This study involved 20 individuals (11 males, 9 females; mean ± SD age 22 ± 3.9 years, weight 76.11 ± 13.48 kg, and height 178.32 ± 12.32 cm) who underwent a previous unilateral ACLR using a patellar tendon autograft. Magnetic resonance images from both extremities were acquired at 6 and 12 months post‐ACLR. Voxel by voxel T1ρ relaxation times were calculated using a 5‐image sequence. The medial and lateral regions of the femoral trochlea and patellar articular cartilage were manually segmented on both extremities. Separate extremity (ACLR and contralateral extremity) by time (6 months and 12 months) analysis of variance tests were performed for each region ( P < 0.05). Results For the medial patella and lateral trochlea, T1ρ relaxation times increased in both extremities between 6 and 12 months post‐ACLR (medial patella P = 0.012; lateral trochlea P = 0.043). For the lateral patella, T1ρ relaxation times were significantly greater on the contralateral extremity compared to the ACLR extremity ( P = 0.001). The T1ρ relaxation times of the medial trochlea on the ACLR extremity were significantly greater at 6 ( P = 0.005) and 12 months ( P < 0.001) compared to the contralateral extremity. T1ρ relaxation times of the medial trochlea significantly increased from 6 to 12 months on the ACLR extremity ( P = 0.003). Conclusion Changes in T1ρ relaxation times occur within the first 12 months following ACLR in specific regions of the patellofemoral joint on the ACLR and contralateral extremity.

ABSTRACT Purpose Greater articular cartilage T1ρ magnetic resonance imaging relaxation times indicate less proteoglycan density and are linked to posttraumatic osteoarthritis development after anterior cruciate ligament reconstruction (ACLR). Although changes in T1ρ relaxation times are associated with gait biomechanics, it is unclear if excessive or insufficient knee joint loading is linked to greater T1ρ relaxation times 12 months post-ACLR. The purpose of this study was to compare external knee adduction (KAM) and flexion (KFM) moments in individuals after ACLR with high versus low tibiofemoral T1ρ relaxation profiles and uninjured controls. Methods Gait biomechanics were collected in 26 uninjured controls (50% females; age, 22 ± 4 yr; body mass index, 23.9 ± 2.8 kg·m−2) and 26 individuals after ACLR (50% females; age, 22 ± 4 yr; body mass index, 24.2 ± 3.5 kg·m−2) at 6 and 12 months post-ACLR. ACLR-T1ρHigh (n = 9) and ACLR-T1ρLow (n = 17) groups were created based on 12-month post-ACLR T1ρ relaxation times using a k-means cluster analysis. Functional analyses of variance were used to compare KAM and KFM. Results ACLR-T1ρHigh exhibited lesser KAM than ACLR-T1ρLow and uninjured controls 6 months post-ACLR. ACLR-T1ρLow exhibited greater KAM than uninjured controls 6 and 12 months post-ACLR. KAM increased in ACLR-T1ρHigh and decreased in ACLR-T1ρLow between 6 and 12 months, both groups becoming more similar to uninjured controls. There were scant differences in KFM between ACLR-T1ρHigh and ACLR-T1ρLow 6 or 12 months post-ACLR, but both groups demonstrated lesser KFM compared with uninjured controls. Conclusions Associations between worse T1ρ profiles and increases in KAM may be driven by the normalization of KAM in individuals who initially exhibit insufficient KAM 6 months post-ACLR.

Osteoarthritis (OA) is the most common disabling joint disease. Magnetic resonance (MR) imaging has been commonly used to assess knee joint degeneration due to its distinct advantage in detecting morphologic cartilage changes. Although several statistical methods over conventional radiography have been developed to perform quantitative cartilage analyses, little work has been done capturing the development and progression of cartilage lesions (or abnormal regions) and how they naturally progress. There are two major challenges, including (i) the lack of building spatial-temporal correspondences and correlations in cartilage thickness and (ii) the spatio-temporal heterogeneity in abnormal regions. The goal of this work is to propose a dynamic abnormality detection and progression (DADP) framework for quantitative cartilage analysis, while addressing the two challenges. First, spatial correspondences are established on flattened 2D cartilage thickness maps extracted from 3D knee MR images both across time within each subject and across all subjects. Second, a dynamic functional mixed effects model (DFMEM) is proposed to quantify abnormality progression across time points and subjects, while accounting for the spatio-temporal heterogeneity. We systematically evaluate our DADP using simulations and real data from the Osteoarthritis Initiative (OAI). Our results show that DADP not only effectively detects subject-specific dynamic abnormal regions, but also provides population-level statistical disease mapping and subgroup analysis.

Sarcopenia, defined as the loss of muscle mass, strength, and function with aging, is a geriatric syndrome with important implications for patients and healthcare systems. Sarcopenia increases the risk of clinical decompensation when faced with physiological stressors and increases vulnerability, termed frailty. Sarcopenia develops due to inflammatory, hormonal, and myocellular changes in response to physiological and pathological aging, which promote progressive gains in fat mass and loss of lean mass and muscle strength. Progression of these pathophysiological changes can lead to sarcopenic obesity and physical frailty. These syndromes independently increase the risk of adverse patient outcomes including hospitalizations, long-term care placement, mortality, and decreased quality of life. This risk increases substantially when these syndromes co-exist. While there is evidence suggesting that the progression of sarcopenia, sarcopenic obesity, and frailty can be slowed or reversed, the adoption of broad-based screening or interventions has been slow to implement. Factors contributing to slow implementation include the lack of cost-effective, timely bedside diagnostics and interventions that target fundamental biological processes. This paper describes how clinical, radiographic, and biological data can be used to evaluate older adults with sarcopenia and sarcopenic obesity and to further the understanding of the mechanisms leading to declines in physical function and frailty.

Background: Excessively high joint loading during dynamic movements may negatively influence articular cartilage health and contribute to the development of posttraumatic osteoarthritis after anterior cruciate ligament reconstruction (ACLR). Little is known regarding the link between aberrant jump-landing biomechanics and articular cartilage health after ACLR. Purpose/Hypothesis: The purpose of this study was to determine the associations between jump-landing biomechanics and tibiofemoral articular cartilage composition measured using T1ρ magnetic resonance imaging (MRI) relaxation times 12 months postoperatively. We hypothesized that individuals who demonstrate alterations in jump-landing biomechanics, commonly observed after ACLR, would have longer T1ρ MRI relaxation times (longer T1ρ relaxation times associated with less proteoglycan density). Study Design: Cross-sectional study; Level of evidence, 3. Methods: A total of 27 individuals with unilateral ACLR participated in this cross-sectional study. Jump-landing biomechanics (peak vertical ground-reaction force [vGRF], peak internal knee extension moment [KEM], peak internal knee adduction moment [KAM]) and T1ρ MRI were collected 12 months postoperatively. Mean T1ρ relaxation times for the entire weightbearing medial femoral condyle, lateral femoral condyle (global LFC), medial tibial condyle, and lateral tibial condyle (global LTC) were calculated bilaterally. Global regions of interest were further subsectioned into posterior, central, and anterior regions of interest. All T1ρ relaxation times in the ACLR limb were normalized to the uninjured contralateral limb. Linear regressions were used to determine associations between T1ρ relaxation times and biomechanics after accounting for meniscal/chondral injury. Results: Lower ACLR limb KEM was associated with longer T1ρ relaxation times for the global LTC (Δ R 2 = 0.24; P = .02), posterior LTC (Δ R 2 = 0.21; P = .03), and anterior LTC (Δ R 2 = 0.18; P = .04). Greater ACLR limb peak vGRF was associated with longer T1ρ relaxation times for the global LFC (Δ R 2 = 0.20; P = .02) and central LFC (Δ R 2 = 0.15; P = .05). Peak KAM was not associated with T1ρ outcomes. Conclusion: At 12 months postoperatively, lower peak KEM and greater peak vGRF during jump landing were related to longer T1ρ relaxation times, suggesting worse articular cartilage composition.

Objective: Biochemical joint changes contribute to posttraumatic osteoarthritis (PTOA) development following anterior cruciate ligament reconstruction (ACLR). The purpose of this longitudinal cohort study was to compare tibiofemoral cartilage composition between ACLR patients with different serum biochemical profiles. We hypothesized that profiles of increased inflammation (monocyte chemoattractant protein-1 [MCP-1]), type-II collagen turnover (type-II collagen breakdown [C2C]:synthesis [CPII]), matrix degradation (matrix metalloproteinase-3 [MMP-3] and cartilage oligomeric matrix protein [COMP]) preoperatively to 6-months post-ACLR would be associated with greater tibiofemoral cartilage T1p relaxation times 12-months post-ACLR. Design: Serum was collected from 24 patients (46% female, 22.1 +/- 4.2 years old, 24.0 +/- 2.6 kg/m2 body mass index [BMI]) preoperatively (6.4 +/- 3.6 days post injury) and 6-months post-ACLR. T1p Magnetic Resonance Imaging (MRI) was collected for medial and lateral tibiofemoral articular cartilage at 12months post-ACLR. A k-means cluster analysis was used to identify profiles based on biomarker changes over time and T1p relaxation times were compared between cluster groups controlling for sex, age, BMI, concomitant injury (either meniscal or chondral pathology), and Marx Score. Results: One cluster exhibited increases in MCP-1 and COMP while the other demonstrated decreases in MCP-1 and COMP preoperatively to 6-months post-ACLR. The cluster group with increases in MCP-1 and COMP demonstrated greater lateral tibial (adjusted mean difference = 3.88, 95% confidence intervals [1.97-5.78]) and femoral (adjusted mean difference = 12.71, 95% confidence intervals [0.41-23.81]) T1p relaxation times. Conclusion: Profiles of increased serum levels of inflammation and matrix degradation markers preoperatively to 6-months post-ACLR are associated with MRI changes consistent with lesser lateral tibiofemoral cartilage proteoglycan density 12-months post-ACLR. (c) 2021 Osteoarthritis Research Society International. Published by Elsevier Ltd. All rights reserved.

Purpose: Aberrant gait biomechanics following anterior cruciate ligament reconstruction (ACLR) are associated with the development of posttraumatic osteoarthritis (PTOA). External knee adduction moment (KAM) is used to estimate medial compartment loading during gait and greater KAM is associated with more severe idiopathic knee osteoarthritis progression. However, there is conflicting evidence regarding the association between KAM and PTOA development. Lesser KAM is associated with worse joint tissue metabolism and articular cartilage composition at 6 months post-ACLR. Others have reported that increased KAM between pre-surgery and 6 months post-ACLR timepoints is associated with deleterious changes in articular cartilage composition over the same time period. The purpose of this study was to determine differences in KAM during gait at both 6 and 12 months post-ACLR between groups of individuals based on tibiofemoral cartilage T1rho MRI relaxation time profiles (a marker of proteoglycan density) at 12 months post-ACLR. Greater T1rho relaxation times were interpreted a tibiofemoral cartilage composition with lesser proteoglycan density and worse cartilage degeneration. A comparison of KAM to uninjured controls was used to contextualize KAM outcomes. Methods: Twenty-six individuals (50% female, 6mo: 22.2±4.0 years, 24.0±3.1 kg/m2 body mass index [BMI]) with unilateral ACLR participated in a two-session (i.e., 6 months post-ACLR and 12 months post-ACLR) longitudinal study. T1rho MRI relaxation times were measured 12 months post-ACLR. KAM was collected at both time points during overground walking at a self-selected speed. KAM was also collected on thirty-four uninjured controls (47% female, 6mo: 21.1±2.9 years, 24.1±2.6 kg/m2 BMI) at a single time point using the same protocol. KAM data were time normalized to 101 unique points of stance phase between heel-strike (vertical ground reaction force [vGRF] > 20 N) and toe-off (vGRF < 20 N) and normalized to the product of body weight (BW) and height (m) (Figure 1a.1 and Figure 1a.2). MRI was collected on a 3T scanner using a T1rho prepared three-dimensional Fast Low Angle Shot (FLASH) sequence with a spin-lock power at 500Hz at five different spin-lock durations (40, 30, 20, 10, 0 ms). Voxel-by-voxel T1rho relaxation maps were constructed from a five-image sequence using an inhouse program. Weight bearing tibiofemoral articular cartilage was manually segmented for the medial (MFC) and lateral femoral (LFC) and tibial (MTC and LTC) condyles. Greater T1rho MRI relaxation times are interpreted as less proteoglycan density. A k-means cluster analysis was used to idenitfy unique groups of individuals based on T1rho relaxation times for the LFC, LTC, MFC, and MTC at 12 months post-ACLR. The cluster analysis led to the identification of individuals with ACLR into two groups with high (T1rhoHigh) and low T1rho relaxation times (T1rhoLow), which is linked to lesser and greater proteoglycan density, respectively. Separate one-way analyses of variance were performed to compare 12-month T1rho MRI relaxation times in each condyle as well as demographic outcomes between final cluster profiles. Functional analyses of variance were conducted to evaluate differences in KAM throughout stance at both 6 and 12 months post-ACLR between T1rhoHigh and T1rhoLow ACLR groups as well as between each ACL group (i.e., T1rhoHigh and T1rhoLow) and uninjured controls. Comparisons between waveforms were considered different at any percentile of the stance phase where mean differences and corresponding 95% confidence intervals did not cross zero (Figure 1b-Figure 1d). Results: T1rhoHigh (n=9, 67% female, 22.2±2.4 years, 23.1±1.2 kg/m2 12mo BMI) relaxation times were significantly greater in the LFC (p=0.013), MFC (p<0.001), and MTC (p=0.001) regions compared to T1rhoLow (n=17, 41% female, 22.7±4.5 years, 24.5±3.7 kg/m2 12mo BMI). There were no significant differences in any demographic variables or self-selected gait speeds between the uninjured controls and ACLR individuals or between T1rhoHigh and T1rhoLow ACLR groups. T1rhoHigh individuals demonstrated lesser KAM between 12-45% and 61-95% of stance compared to T1rhoLow individuals at 6 months post-ACLR (Figure 1b.1). Similarly, T1rhoHigh individuals demonstrated lesser KAM between 18-31%, 60-63%, and 70-84% of stance compared to T1rhoLow individuals 12 months post-ACLR (Figure 1b.2). T1rhoLow individuals demonstrated greater KAM through the majority of stance at 6 months (4-49% and 66-100%; Figure 1c.1) and 12 months (5-12% and 20-91%; Figure 1c.2) post-ACLR compared to uninjured controls. T1rhoHigh individuals demonstrated lesser KAM through the majority of stance at 6 months (17-31% and 74-89%) compared to uninured controls (Figure 1d.1). There were no differences in KAM between T1rhoHigh and the uninjured controls 12 months post-ACLR (Figure 1d.2). Conclusions: Individuals with high T1rho MRI profiles (i.e., lower proteoglycan density) at 12 months post-ACLR exhibited lower than normal (i.e., relative to uninjured controls) KAM at 6 months post-ACLR. Additionally, ACLR group with high T1rho MRI profiles exhibiting the lowest KAM at 6 months post-ACLR tended to increase KAM between 6 and 12 months post-ACLR. Yet, the ACLR group with high T1rho MRI did not demonstrate KAM magnitudes that exceeded the uninjured-controls at 12-months post-ACLR, suggesting KAM underloading at 6 months post-ACLR is linked to high T1rho MRI profiles following ACLR. Future research should evaluate the potential protective influence of greater KAM early following ACLR on maintaining articular cartilage health.

Purpose: There is a growing recognition of the heterogeneity of the OA disease process, generating a need for new methodologies to better characterize potentially important subgroups. The purpose of this work is to utilize novel methodologies to explore potential phenotypic groups within the baseline Osteoarthritis Initiative (OAI) data, incorporating demographic and clinical features and knee cartilage maps. Methods: From the OAI baseline dataset (n=4796 individuals with or at risk of knee OA/9592 knees; 116 clinical and demographic features; https://nda.nih.gov/oai/; AllClinical00), we excluded uninformative variables and features/knees with missing data leaving 86 features from 3322 people (6461 knees) for analyses. Continuous variables were standardized and transformed using a procedure to remove skewness. Of these, 6390 knees had available femoral and tibial cartilage maps for the enrollment timepoint. This baseline dataset of knees was divided into two equal partitions and the analysis repeated for each for internal validation purposes. Femoral and tibial cartilage was segmented from the 3D DESS MR images using a 3D U-Net for all patients and timepoints in the OAI dataset. Thickness was measured to the closest point of the opposing surface of the segmented cartilages and transferred to an atlas space via deep-learning-based deformable image registration, resulting in local spatial correspondences between all patients and all timepoints. The thickness maps in this atlas space were then unrolled/projected to a 2D plane. For this analysis, we utilized baseline maps and associated data. Angle-based Joint and Individual Variation Explained (AJIVE) is a method for understanding the modes of variation expressed in multiple sets of measurements, called data blocks. This work includes 3 blocks (or data types): (i) femoral cartilage maps (ii) tibial cartilage maps, and (iii) clinical and demographic features from the OAI, all in the same set of knees. AJIVE captures (1) shared (or “joint”) structure between blocks and (2) structure individual to each block. Here, we focus on exploring the ways in which these blocks (different data types) vary together (shared, or “joint” variation), that may reflect features of OA. Here, we focus on exploring the ways in which these blocks (different data types) vary together (shared, or “joint” variation), that may reflect features of OA. The modes of variation are visualized using loadings plots, which show the contribution of each cartilage pixel and of each OAI variable to the mode of variation. The amount that each knee expresses each of these modes is called a score. Each knee’s score for each direction is represented by a dot, colored according to baseline Kellgren-Lawrence Grade (KLG, from 0-4).Statistical significance of the number of joint modes and of the contribution of each variable to those modes (joint loadings) were assessed. Statistical significance of the loadings (i.e., whether a particular cartilage pixel or OAI feature has a nonzero influence on a mode of variation) was assessed using a modification of the jackstraw method (Chung and Storey) for principal component analysis. We show only the loadings that are statistically significantly nonzero at the 0.01 level. Results: This analysis includes n=6390 knees, each of which have (i) a 57,117-pixel femoral cartilage reconstruction, (ii) a 56,204-pixel tibial cartilage reconstruction, and (iii) 86 measurements from the OAI, including basic vitals, demographics, pain surveys, and measurements of knee function. The top three directions of shared/joint variation between these three datasets were consistent in the two partitions (providing internal validation) and are discussed below and shown in the Figure. Direction 1 (Column 1 in Figure) reflects features that are associated with thicker baseline cartilage overall and is bimodal based on sex (1A). Knees with thicker (red) femoral (1B) and tibial (1C) cartilage are characterized by higher knee flexion force and speed of force production, male sex, higher education, faster 400m walk times (fewer seconds), and fewer medications (1D). Particularly for Direction 2, there is a clear association between worsening KLG (2A-B) and cartilage thinning (blue) in the medial femur (2C) and most of the tibia (2D). Features associated with variation in this direction include greater knee flexion contracture, poorer symptoms (lower KOOS and higher WOMAC), higher BMI, older age, and poorer SF-12 physical health (2D). Direction 3 represents a spectrum between lateral-predominant thinning and medial predominant thinning (3D-E), where lateral thinning is associated with valgus knee alignment, higher BMI, fewer chair stands completed per second, Black race, slower walking speeds, and lower physical activity (PASE) scores (3F), while medial thinning is associated with varus alignment, lower BMI, etc. Conclusions: These Results utilize all the OAI baseline data in a novel analysis which provides directions of joint, or shared variations. The directions reflect 1) overall thicker cartilage in men and in healthier participants (greater knee flexion forces, less medication, faster walk times); 2) medial thinning in older individuals with higher BMI, poorer physical health, and more symptoms; 3) lateral thinning in Black individuals with higher BMI, lower physical activity, slower walking speeds, and valgus alignment. These directions are interpretable, reflecting known aspects of knee OA, but also provide additional associations by utilizing all of the available data. This cross-sectional analysis is a first step, with future directions to include assessing the directions of individual variation (for the femoral and tibial cartilage and the features) and considering the impact of baseline individual and joint variation on prognosis.

Purpose: A complex association exists between aberrant gait biomechanics and the development of posttraumatic osteoarthritis (PTOA) following anterior cruciate ligament reconstruction (ACLR). Therefore, a comprehensive understanding of the association between gait biomechanics and tibiofemoral articular cartilage composition is needed to develop the most appropriate biomechanical interventions to prevent PTOA post-ACLR. Recent waveform analyses demonstrate that differences in gait biomechanics exist between ACLR patients and uninjured, matched-controls at multiple portions of the stance phase in the first 12 months post-ACLR. Specifically, ACLR individuals demonstrate lesser vertical ground reaction force (vGRF) in early (i.e., 1-30% of stance) and late stance (i.e., 70-100% of stance) and greater vGRF during midstance (i.e., 33-64% of stance) compared to uninjured counterparts. Unfortunately, the majority of previous literature has focused on the influence of peak loading in early stance on PTOA development, leading to a dearth of knowledge regarding the link between mid and late stance loading and PTOA development. Therefore, the purpose of this exploratory study was to determine the associations between vGRF, a biomechanical indicator of lower limb loading, throughout the entirety of stance phase with in vivo estimates of tibiofemoral articular cartilage proteoglycan density using T1rho magnetic resonance imaging (MRI) relaxation times in individuals who were 12 months post-ACLR. Methods: Twenty-three participants (48% female, 22.09±4.09 years old, 24.18±3.30 kg/m2 body mass index) with unilateral ACLR participated in this cross-sectional study. vGRF was collected barefoot during an overground walking task at self-selected walking speed across a 6m walkway. vGRF data were time normalized to 101 unique points of stance phase between heel strike and toe off and normalized to body weight (BW). MRI was collected on either a Siemens Magnetom TIM Trio 3T or a Siemens Magnetom Prisma 3T scanner using a T1rho prepared three-dimensional Fast Low Angle Shot (FLASH) sequence with a spin-lock power at 500Hz at five different spin-lock durations (40, 30, 20, 10, 0 ms). Voxel-by-voxel T1rho relaxation maps were constructed from a five-image sequence using an in-house program. Anterior, central and posterior regions of interest (ROI) were manually segmented from the weightbearing the articular cartilage of the medial (MFC) and lateral femoral (LFC) and tibial (MTC and LTC) condyles. ROI were determined by the location of the meniscus in the sagittal plane and included: 1) the articular cartilage communicating with the anterior horn of the meniscus (anterior MFC/LFC and MTC/LTC), 2) the central portion of the articular cartilage between the anterior and posterior horns of the meniscus (central MFC/LFC and MTC/LTC), and 3) the articular cartilage communicating with the posterior horn of the meniscus (posterior MFC/LFC and MTC/LTC). Primary analysis utilized a global weightbearing score for T1rho relaxation times averaged across the three ROI (anterior, central and posterior) for each condyle. Greater T1rho MRI relaxation times are interpreted as lesser cartilage proteoglycan density. In an exploratory manner, we conducted our primary analyses with separate bivariate, Pearson Product Moment correlation coefficients (r) between T1rho relaxation times for each global region and vGRF at each 1% increment of stance phase (1-101%). Additionally, we constructed corresponding 95% confidence intervals (CI) for all Pearson Product Moment correlation coefficients at each 1% of stance phase using a Fisher’s transformation. We recognized associations as weak (r=0.0 to 0.3), moderate (r=0.3 to 0.5) or strong (r>0.5). We focused the discussion of our primary analyses on associations demonstrating Pearson Product Moment correlation coefficients with 95% CI that did not include zero. For easy visualization purposes, we presented the magnitude of Pearson Product Moment correlation coefficients and corresponding 95% CI (y-axis) at each percentage of stance (x-axis) for each of the femoral and tibial condyles (Fig. 1). If 95% CI were found not to cross zero for a correlation coefficient in primary analyses of global weightbearing regions, we conducted secondary Pearson Product Moment correlation analyses in the same manner as our primary analyses to further determine whether T1rho MRI relaxation times in a specific weightbearing ROI (anterior, central, posterior) were associated with vGRF. Results: Greater vGRF during the midstance of gait (46-56% of stance phase) was associated with greater MFC T1ρ MRI relaxation times (r ranging between 0.43 and 0.46) with corresponding 95% CI that did not include zero (Fig. 1a). Secondary analyses demonstrated that only weak associations (r ranging between -0.29 and 0.29) were observed between vGRF and anterior MFC. Greater vGRF was associated with greater T1rho MRI relaxation times in the central MFC (r ranging between r=0.43 and 0.50) during midstance (45-55% of stance), while lesser vGRF was associated with greater T1rho MRI relaxation times in the central MFC (r ranging between -0.43 and -0.45) during late stance (74-78% of stance). Greater vGRF was associated with greater T1rho MRI relaxation times in the posterior MFC (r ranging between 0.43 and 0.54) during midstance (36-53% of stance). Weak to moderate associations were found in the LFC (Fig. 1b), MTC (Fig. 1c), and LTC (Fig. 1d) regions; however, all associated 95% CIs included zero. Conclusions: Greater lower extremity loading during midstance demonstrated the strongest associations with articular cartilage T1rho MRI relaxation times in the MFC. Our secondary analyses further demonstrate that the association between greater vGRF during midstance and greater T1rho MRI relaxation times was strongest in the central and posterior MFC ROIs. Adequate loading and unloading of articular cartilage are critical to maintain optimal articular cartilage health. Our data suggest that inadequate unloading of the articular cartilage during midstance is linked to lesser proteoglycan density in the MFC post-ACLR. Previous data demonstrate that individuals with ACLR walk with a less dynamic vGRF waveform compared to uninjured, matched-controls, characterized by lower vGRF peaks in early and late stance and greater vGRF in midstance. Therefore, it is possible that dynamically unloading the lower extremity during midstance may result in optimal MFC articular cartilage health and promote greater proteoglycan density within the tissue. Overall, our study suggests the relation between gait biomechanics and tibiofemoral articular cartilage composition varies across stance phase and future work should evaluate the association between midstance kinetics and PTOA development.